Dynamic motion contrast and transverse flow estimation using optical coherence tomography

ABSTRACT

The methods described herein are methods to ascertain motion contrast within optical coherence tomography data based upon phase variance. The phase variance contrast observes the nanometer scale motion of scatterers associated with Brownian motion and other non-flow motion. The inventive method of calculating motion contrast from the phase variance can differentiate regions of different mobility based on the motion contrast differences, and can use the phase information to characterize mobility properties of the scatterers. In flow regions, the inventive method for acquiring and analyzing motion contrast can identify the regions as well as characterize the motion. Furthermore, the inventive method can determine quantitative flow estimation, the index of refraction variations, and absorption variations within flow regions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Ser. No. 11/767,187 filedJun. 22, 2007, now issued as U.S. Pat. No. 7,995,814, which claims thebenefit under 35 U.S.C. §119(e) of U.S. Ser. Nos. 60/816,431, filed onJun. 26, 2006, and 60/853,684, filed on Oct. 23, 2006, the contents ofall of which are incorporated by reference herein in their entirety.

FIELD OF THE INVENTION

Optical coherence tomography (OCT) is an optical imaging technique thatallows for three dimensional visualization and analysis of structures ina variety of biological systems that are difficult to examine andanalyze with other imaging techniques. The embodiments of this inventiondescribe methods to produce motion contrast for a variety of motiontypes in an OCT system by acquiring and analyzing data according to theinventive method based on phase variance and/or intensity fluctuationanalysis.

BACKGROUND OF THE INVENTION

OCT is a non-invasive optical imaging technique which producesdepth-resolved reflectance imaging of samples through the use of a lowcoherence interferometer system. OCT imaging allows forthree-dimensional visualization of structures in a variety of biologicalsystems not easily accessible through other imaging techniques,including but not limited to the retina of the eye.

Vascular visualization and quantitative information of blood flow isvery important for the diagnosis and treatment of many diseases. In OCTimaging, a type of phase sensitive analysis called Doppler OCT is theprimary form of vascular visualization and diagnostic. Phase is a typeof high resolution position measurement of a reflection along theoptical path length of the imaging system, which is cyclic of thefrequency of half the wavelength of the imaging light. A depth positionchange of half the imaging wavelength will produce the same phasemeasurement. Changes in phase are proportional to the axial flow, theflow component parallel to the imaging direction designated by v(cos θ),where v is the velocity of the flow and θ is the angle between the flowdirection and the imaging light. Phase noise in the system based on thelocal signal to noise ratio determines the minimum axial flowmeasurement, which limits the visualization of flow in cases where v orcos θ is very small. For cases such as in the retina, a lot of flow isnearly perpendicular to the imaging direction such that θ˜90° and cosθ˜0. In these cases, the velocity v of the flow must be extremely highto be able to visualize the flow with this method.

The progress of development in OCT is towards faster imagingtechnologies and techniques in order to image larger regions in the sameamount of time. In order to maintain fast imaging speeds, Doppler OCTimaging techniques only use a few successive depth reflectivitymeasurements called A-scans (the typical number is around 5), andaverage the phase change between each of them. The limited statisticsand the short time between the A-scans (and also the phase measurements)severely limit the minimum observable axial flow, allowing for onlyvisualization of the fastest flows.

Demonstrated variance calculations of the phase changes for thissituation do not add additional motion contrast to the images. This lackof additional contrast is because the phase error due to the localsignal to noise ratio dominates the calculations in all regions exceptin the same fast flow regions visualized with the Doppler OCT technique.

Speckle analysis looks at intensity variations of images, which haslimited work demonstrated in the field of OCT. Most of the work withspeckle in OCT has been directed towards the reduction of speckleartifacts from multiple reflections within the sample to improve imagequality. Demonstrated speckle analysis techniques utilize spatialvariations of intensity from a single static image to characterizeregions and identify regions of flow. These techniques are only capableof analyzing regions much larger than the spatial resolution of theimaging system and have typically been used in non-OCT imagingsituations which do not have the depth discrimination capabilities ofOCT.

Accordingly, there is a need in the field of OCT for an accurate andefficient method to ascertain flow of biological fluids to assist in thediagnosis and treatment of many diseases. In particular, there is a needto develop a method capable of estimating transverse flow velocity andto ascertain motion contrast with OCT systems.

SUMMARY OF THE INVENTION

This application describes the acquisition and analysis methods toproduce motion contrast based on phase variance and intensityfluctuation and/or speckle information in an OCT system for a variety ofmotion types. The phase variance contrast utilizes the temporalevolution of the measured phase variance of the motion to identify andcharacterize mobile scatterers within the OCT sample images. Acquisitionmethods are presented which demonstrate a highly efficient acquisitioncapable of screening for regions of mobility as well as an acquisitioncapable of quantitative diagnostics of the scatterers.

In another embodiment, the method further comprises using the temporalfluctuations in intensity of the OCT images. The temporal fluctuationsin intensity of the OCT images can also be used as another form ofcontrast to observe the fluctuations associated with flow and absorptionchanges within the depths of the sample.

The methods and techniques developed herein demonstrate motion contrastwithin optical coherence tomography images. The motion contrast, and inparticular the phase variance contrast is able to observe the nanometerscale motion of scatterers associated with Brownian motion and othernon-flow motion. The contrast calculated from the phase variance candifferentiate regions of different mobility based on the differences inmotion contrast and can use the phase information to characterizemobility properties of the scatterers. For flow regions, the analyticalmethods described can identify the regions as well as characterize themotion. Quantitative flow estimation can be determined for flow,independent of the orientation relative to the imaging direction. Theshadowing of contrast in the phase variance and the intensityfluctuation calculations can be used to determine the index ofrefraction variations as well as absorption variations within flowregions.

The acquisition methods described herein demonstrate a low efficiency,highly informative diagnostic acquisition as well as an efficient,three-dimensional screening acquisition. The highly efficientacquisition allows for three-dimensional visualization of mobile regionssuch as a vascular region within the sample. The flexibility of theseinventive methods allow for the identification of regions ofintermittent flow, such as identification of blood cell motion in themicrovasculature. The ability to identify regions of intermittent bloodflow can assist in the diagnosis and treatment of a patient in needthereof.

A computer readable medium having computer executable instructions forascertaining motion contrast in a sample is also contemplated herein.The computer readable medium having computer executable instructions forascertaining motion contrast comprises acquiring data using an OCTsystem by performing one or more scan of the sample, ascertaining phasevariance of the data, and ascertaining motion contrast in the samplebased upon the phase variance. The computer readable medium havingcomputer executable instructions for ascertaining motion contrast mayfurther utilize intensity fluctuation and/or speckle information forascertaining motion contrast in a sample. The phase variance contrastmay utilize the temporal evolution of the measured phase variance of themotion to identify and characterize mobile scatterers within the OCTsample images. The computer readable medium having computer executableinstructions for ascertaining motion contrast may also includeacquisition methods capable of screening for regions of mobility andquantitative diagnostics of the scatterers.

An OCT machine comprising computer readable media having computerexecutable instructions for ascertaining motion contrast in a sample isalso contemplated herein. The computer readable medium having computerexecutable instructions for ascertaining motion contrast on the OCTmachine comprises acquiring data using the OCT system by performing oneor more scan of the sample, ascertaining phase variance of the data, andascertaining motion contrast in the sample based upon the phasevariance. The OCT machine comprising computer readable media havingcomputer executable instructions for ascertaining motion contrast mayfurther utilize intensity fluctuation and/or speckle information forascertaining motion contrast in a sample. The phase variance contrastmay utilize the temporal evolution of the measured phase variance of themotion to identify and characterize mobile scatterers within the OCTsample images. The OCT machine comprising computer readable media havingcomputer executable instructions for ascertaining motion contrast mayalso include acquisition methods capable of screening for regions ofmobility and quantitative diagnostics of the scatterers.

DETAILED DESCRIPTION OF THE INVENTION

The OCT system used for the data presented herein is a spectral domainoptical coherence tomography (SDOCT) setup as shown in FIG. 1, with afiber-optic interferometer used to split the light between a referencearm and a sample arm. The acquisition and analysis techniques describedherein do not depend on the type of OCT system used, only on the numberof intensity and phase samples taken as well as the speed of each depthreflectivity measurement, called an A-scan.

The phase change Δφ(z_(i),T) measured for a given depth z_(i) for a timeseparation T is a combination of several factors effecting themeasurement:Δφ(z _(i) ,T)=Δφ_(motion,scatterer)(z _(i),T)+Δφ_(motion,bulk)(T)+Δφ_(error,SNR)(z _(i))+Δφ_(error,other)(z _(i))

The phase change Δφ(z_(i),T) contains not only the individual motion ofthe scatterer at the depth z_(i) which is designated byΔφ_(motion,scatterer)(z_(i),T) (which is the motion of interest), but italso contains the bulk relative motion between the sample and the systemalong the imaging (axial) direction Δφ_(motion,bulk)(T).Δφ_(error,SNR)(z_(i)) is the phase error associated with the SNR of thedata calculated at the depth z_(i). Published experimental results havecalculated that the accuracy of measured phase changes is determined bythe local signal to noise ratio and is of the form:σ_(Δφ,SNR) _(—) _(error)(z)−1/√{square root over (SNR(z))}Δφ_(error,other)(z_(i)) encompasses the other phase errors which mayoccur for OCT phase measurements, including but not limited totransverse scanning errors, transverse motion of the sample, orartifacts associated with limited depth sampling during axial motion ofthe sample. To be able to identify the motion of the scatterers for agiven depth Δφ_(motion,scatterer)(z_(i),T), the effects of the otherforms of phase noise need to be removed or reduced. Each phasemeasurement calculated the relative motion between a depth reflection ofthe sample and the rest of the imaging system. From a single phasechange measurement, it is not possible to separate the bulk relativeaxial motion between the sample and system from the individual motion ofa mobile region within the sample. Without the ability to remove thisbulk motion, the minimum measurable motion within the sample is limitedby the bulk system and sample motion.

One of the methods of determining the bulk sample motion is using anadditional motion measurement, like an interferometer for example, todetermine the motion of the strongest reflection within the sample. WithFourier domain optical coherence tomography systems, which includesspectral domain optical coherence tomography and swept source opticalcoherence tomography (also referred to as optical frequency domainimaging), all of the depths of the sample are measured at the same time.With the phase change information available for all of the depthreflections, it is possible to extract the bulk motion for removal.

In some scenarios, there is a strong reflector within the sample thatcan be used as a stationary reflection to be used as a reference for thebulk phase removal. Many cases do not contain a highly reflectivestationary reflector to use, so the entire sample depth must be used tocalculate the bulk motion. There are several ways to analyze the phasechange information from all of the depths to calculate the bulk samplemotion. With the large phase noise associated with depths with noreflectance or signal near the noise level, the mean calculated from allof the phase changes can be distorted due to these low signal terms.Thresholding of the phase data analyzed can reduce the effect of theseterms. The mode of the calculated phase changes can be used to determinethe bulk motion as well, with an accuracy determined by the parametersused in the mode calculation.

A weighted mean calculation allows for the estimation of the bulk motionfor a variety of sample cases. The bulk motion in this method iscalculated by Δφ_(motion,bulk)(T)Σ[w(z_(i))Δφ(z_(i),T)]/Σ[w(z_(i))],where the weighting factor w(z_(i)) is dependant on the type of imagingsituation. The weighting factor can be determined by the linear OCTintensity I²(z_(i)), which relies on the highest reflections within thesample being stationary. Using the weighting factor of the OCT amplitudeI(z_(i)), the calculation is more sensitive to the phase noise of thelow signal terms within the measurement. By incorporating a thresholdterm into the weighting factor, the phase noise effect from the lowsignal terms can be reduced in all cases. The weighting term can alsocontain a spatial dependence to deal with specific sample and motionproperties. For example, the stationary regions of a sample below a highvelocity flow region can appear non-stationary with the phase changemeasurements which can require a different weighting above and below theflow regions within the sample. The weighting factor can alsoincorporate weighting based on the shape of the local intensity functionof the depth reflectivity measurement. Artifacts created by phase motioncalculations of the side lobes and dips of reflectance profiles cancause unwanted discrepancies which can distort the bulk motionestimation.

With the cyclic nature of the phase measurements, phase changes areforced to be limited between −π and +π. Motions larger than this amount(equivalent to a quarter of the wavelength of the imaging light) sufferfrom phase wrapping and are miscalculated (a phase change of +π+δ ismisinterpreted as −π+δ). In phase measurement cases where the bulkmotion of the sample is approximately +/−π, the phase error will causethe calculated phase change distribution to appear similar to the datapresented in FIG. 2. Without additional correction to the phase changedistribution, the calculated bulk motion will be inaccurate. Before bulkmotion calculations occur as described above, the distribution of phaseshould be re-centered such that the mean more accurately represents themotion.

After the removal of the estimated bulk motion from the calculated phasechanges Δφ(z_(i),T)−Δφ_(motion,bulk)(T), the variance of this quantitycan be approximated by the sum of the variance of the individualcomponents composing the phase change:σ_(Δφ) ²(z _(i) ,T)=σ_(Δφ,motion) _(—) _(scatterer) ²(z _(i),T)+σ_(Δφ,SNR) _(—) _(error) ²(z _(i))+σ_(error) _(—) _(other) ²(z _(i))

The motion of interest as the source of contrast for the phase varianceanalysis is the scatterer motion phase variance term σ_(Δφ,motion) _(—)_(scatterer)(z_(i),T). The SNR-limited phase error σ_(SNR) _(—) _(error)² (z_(i)) is determined by the local signal to noise ratio as describedearlier and is independent of the time separation of the phasemeasurement T. The last term of the calculated phase variance σ_(error)_(—) _(other) ²(z_(i)) incorporates all of the other phase error factorswhich include the contributions from the error created from the bulkmotion calculation method as well as all of the other effects describedfor Δφ_(error,other)(z_(i)). The SNR-limited phase error is generallythe limiting factor to visualizing the motion of the scatterers.

There are several types of motion which contain components that areobservable through the motion scatterer variance measurement of thephase motion which include, but are not limited to:

Variations in the axial component of flow

Transverse flow effects of uncorrelated scatterers

Axial component of Brownian-type random motion

Ensemble statistical effects of uncorrelated scatterers (multiplescatterers located within the imaging resolution of the system andidentified as a single scatterer location)

Each of the above mentioned types of motion have a variance of motionwhich increases with the time separation between position (phase)measurements. In most cases, the SNR-limited phase error associated witheach measured reflection is the limiting factor to the minimum scatterermotion that can be observed. Since this phase error is independent oftime, waiting longer between phase measurements allows for the varianceof the scatterer motion to increase beyond the limits of the phaseerror. Further increases to the time separation used for the phasechanges will continue to increase the measured phase change variance.This will continue until the calculated phase variance reaches a levelcomparable to a completely random phase signal. Further increases to themotion of the scatterers should not increase the measured phase variancebeyond the completely random phase.

One of the effects that occurs with even longer time separationsassociated with transverse flow is an appearance of motion shadowingbelow the regions of flow. This is due to the index of refractionchanges which occur within regions of transverse flow. The phasemeasurement in OCT is not simply a change in the position of a givenreflector; it is the change in the optical path length to that samereflector. Therefore, a phase change also measures all of the refractiveindex variations which have occurred during the time separation T overthe entire depth until the measured reflection.

${{\Delta\phi}\left( {z,T} \right)} = {\frac{4\pi}{\lambda_{0}}\left( {{\int_{0}^{z}{\Delta\;{n\left( {z^{\prime},T} \right)}{\mathbb{d}z^{\prime}}}} + {{n(z)}\Delta\; z}} \right)}$For a stationary reflector measured below a region of average refractiveindex change Δ n(z′,T) which extends a depth of z_(n), the calculatedphase change is:

${{\Delta\phi}\left( {z,T} \right)} = {\frac{4\pi}{\lambda_{0}}\Delta\;{\overset{\_}{n}(T)}z_{n}}$For example, to create a completely random phase measurement measuredbelow flow of a vessel of thickness 15 μm, the required minimum averagerefractive index variation in the case of an imaging wavelength ofapproximately 800 nm:

${\frac{\Delta\;{{\overset{\_}{n}}_{rms}(T)}}{\overset{\_}{n}} \approx 0.006} = {0.6\%}$With the knowledge of the time separation of the onset of refractiveindex shadowing within the phase contrast images and knowledge of therefractive indices of the constituents within the flow region,characteristics of the transverse flow and the density variations of theflow can be determined. Accordingly, estimating refractive indexvariations within flow regions assists in ascertaining motion contrastin an optical coherence tomography system.

To demonstrate the increase of measured variance motion with increasedtime separation between phase measurements, the case of Brownian motionis shown. 2% agarose wells are created and filled with an Intralipidsolution diluted to match the scattering intensity of the agarose. Theagarose is a gelatin which is expected to be stationary in comparison tothe mobile Intralipid scatterers.

The phase variance is calculated for this image region for differenttime separations of the phase change. The images presented in FIGS. 5,6,and 7 demonstrate a successive increase in the time separation, startingfrom the shortest time capable with the experimental imaging system. Theshortest time separation phase variance images are dominated by theSNR-limited phase noise of the image. As the time separation increases,the phase variance calculation of the region containing the mobileIntralipid solution increased as well.

In order to properly image the contrast created by the scatterer motion,the SNR-limited phase noise should be removed. One of the methods toremove it is to use phase variance calculations from different timeseparations T₁ and T₂. If we can assume the phase error from othersources is negligible, the calculated phase variance for these timeseparations is of the form:σ² _(Δφ)(z _(i) ,T ₁)≅σ² _(Δφ,scatterer)(z _(i) ,T ₁)+σ² _(Δφ,SNR)(z_(i))σ² _(Δφ)(z _(i) ,T ₂)≅σ² _(Δφ,scatterer)(z _(i) ,T ₂)+σ² _(Δφ,SNR)(z_(i))By choosing the parameters T₂=βT₁ where β>>1, it is assumed that for themotion of the scatterers within the system σ² _(Δφ,scatterer)(z, T₂)>>σ²_(Δφ,scatterer)(z, T₁). The basic phase contrast metric used to createthe phase variance contrast image in this case is chosen to be σ²_(Δφ)(z, T₂)−σ² _(Δφ)(z, T₁) such that:σ² _(Δφ)(z,T ₂)−σ² _(Δφ)(z,T ₁)≅σ² _(Δφ,scatterer)(z,T ₂)With the form known for the SNR-limited phase noise, numerical estimatesof the expected values can be used to eliminate them from the phasevariance images. Numerical estimates of the phase noise are based on theOCT intensity signal of the reflection and the noise properties of theimaging system as described earlier. This case demonstrates similarcontrast images based on the accuracy of the noise estimation.

Looking at the time evolution of the phase variance change can allow forthe characterization of the mobility of the scatterers. FIG. 10demonstrates data acquired from phase variance calculations fordifferent time separations of the Brownian motion of several differentsized microspheres in water, a single scatterer in water with diametersranging from 0.5 μm to 5 μm. Phase variance data is a way to visualizethe random motions due to thermal fluctuations. The expected averagemotion of these scatterers over time equals zero, requiring the variancecalculation to image the motion. The 0.5 μm and 5 μm diameter casesshown are for cases with high OCT intensity signal, so the phase errorfor these phase measurements were negligible relative to the scatterermotion. The case of the 2 μm diameter sphere was chosen for its low OCTsignal, where the phase errors in variance measurements were notnegligible. While the expected form of the motion should follow parallelwith the other cases, the phase error combines with the motion toproduce the form shown in FIG. 10. With enough data measuring phasevariance data against time separation of phase changes, the motion datacan be extracted from the combined phase variance data.

For the phase variance data measured of the Brownian motion (the datawhich is less than the random phase variance saturation), the calculatedmotion is the combination of the time insensitive phase error and theBrownian motion which is of the form:σ_(Δφ) ²(z _(i) ,T)=A ² +DT ^(γ)By fitting the phase variance data over time to the expected form of themotion, the characteristic motion parameter D (the diffusion constant)of the scatterers can be determined.Data Acquisition Methods

In order to acquire the data required to identify and/or characterizeregions of mobility, phase information from large time separations isrequired. One of the simplest acquisition methods is to wait at eachtransverse location, acquiring phase information over time, waiting longenough to acquire the required statistics and the temporal informationof the phase change evolution. In terms of OCT scan terminology, anA-scan is the term for a single depth reflectivity measurement. MultipleA-scans acquired at the same transverse location over time is referredto as an M-scan. Multiple A-scans acquired over a range of transverselocations is referred to as a B-scan. The process of waiting at eachtransverse location creating M-scans, and repeating this over a range oftransverse location will be called a MB-scan for this application.

The MB-scan is the easiest way to acquire the statistics and temporalevolution information of the phase variance in order to characterize themobility of the scatterers. This characterization includesquantification of the flow within regions, along the imaging directionas well as transverse to it (described later). Other characterizationcapabilities with this information include factors such as the diffusionconstant, the scatterer density, and statistical flow information. Theonly limitation to this method is the inefficiency of the acquisition.The time required to visualize a three-dimensional spatial region withthe temporal information of this method would be too long for someapplications. A faster acquisition method capable of some of the phasevariance contrast visualization is required for screening largethree-dimensional regions. Instead of waiting at one location over timeuntil the motion is large enough to produce motion variance contrast,data can be acquired across multiple locations before coming back to theoriginal locations to get additional phase information. This acquisitionmethod is described by acquiring multiple B-scans over time for the sametransverse region, which will be called a BM-scan for this application.

The BM-scan is a very efficient method of acquiring phase informationwith a large time separation without sacrificing total acquisition timeof the image. There is limited temporal information with thisacquisition method, but the phase information which is acquired allowsvisualization of slow motions not visible with analysis from successiveA-scan acquisitions. Quantitative flow analysis with this method islimited in comparison to the MB-scan. A schematic of some examples ofthese acquisition methods is shown in FIG. 11.

Images created using one form of the MB-scan and the BM-scan over thetail of a zebrafish are demonstrated in FIGS. 12 and 13. While the phasevariance contrast images in both figures identify the same regions ofmobility, the BM-scan produces contrast more than 4 times stronger thanthe MB-scan. This is not unexpected considering that the BM-scan usesphase changes for time separations 40 times larger than used for thepresented MB-scan. The contrast image for the BM-scan demonstratesshadowing of the phase variance contrast image due to the indexvariations of the flow regions associated with the large timeseparations.

The cases demonstrated in FIGS. 12 and 13 compare the same region of thesample. The BM-scan demonstrated above contains approximately 2.5 timesmore transverse pixels in a total imaging time reduced by a factor of 3compared to the above MB-scan. BM-scan acquisition times can be reducedfurther by the adjustment of the number of transverse locations,adjustment of the statistics used to calculate the phase changevariance, reduction of the A-scan acquisition time of the system and byimproving the transverse scanning capabilities of the system.

Transverse Flow Estimation Methods

Park et al. [1] published results for the expected phase error to occurwhile transverse scanning over uncorrelated sample reflections as afunction of the fraction of the beam diameter scanned between successivephase measurements. According to the Park definition, standard deviationof the phase difference is equivalent to the square root of what istermed phase variance herein.

In order to create phase contrast in the shortest amount of acquisitiontime, the analysis performed in Park was to determine the limitations ofphase contrast between successive A-scans while scanning transverselyand creating a B-scan. What was not mentioned was the possibility ofusing this expected phase error for relative transverse motion betweenthe sample illumination and the sample reflections as a quantitativemeasure of transverse flow.

If several A-scans are performed at the same transverse positionseparated by a time T, there will be a phase noise term which appearsdue to relative transverse motion between the sample illumination andthe sample reflections. With the illumination at the same transverseposition at successive measurements, the noise term comes fromtransverse flow, particularly from uncorrelated reflections such asthose found in blood. Consider the case of a Gaussian illumination beamwith a 1/e² beam width=d at the focus, and using phase measurementstaken at the same transverse position in the same separated by T. Thevariance of the phase changes is determined for a transverse motion ofthe scatterer Δx between the phase measurements, created by a transversevelocity v_(x) during the time separation T. Defining the fraction ofthe beam width as Δx/d, the variance of the phase error due to thetransverse motion is calculated to be:

$\sigma_{\Delta\phi}^{2} = {{\frac{4\pi}{3}\left( {1 - {\exp\left( {{- 2}\left( \frac{\Delta\; x}{d} \right)^{2}} \right)}} \right)} = {\frac{4\pi}{3}\left( {1 - {\exp\left( {{- 2}\left( \frac{v_{X}T}{d} \right)^{2}} \right)}} \right)}}$

Due to the other error terms which occur in the phase measurements, theaccuracy of this technique may be limited to the accuracy of thecalibrations of the system and the removal of other phase error terms.From the data presented in Park, the dynamic range of quantitativemeasure of transverse flow with this analysis appears to be limited tomotion which is approximately 20%≦Δx/d≦80% in the time period T.SNR-limited phase noise limit the minimum transverse velocity which canbe determined with this method. The upper limit was approximated by thesaturation limit of a random phase noise signal limited to be between −πand π. This equation only allows for qualitative measures of flow suchthat v_(x)T/d>˜0.8. Increased temporal information can improve thedynamic range of this transverse flow measurement.

In the example of retinal imaging, with a time separation of 40 ms and afocused beam diameter of 20 μm, the dynamic range of quantitativetransverse flow calculations is approximately 0.1 mm/s to 0.4 mm/s.

Altering the dynamic range of quantitative flow can be achieved bychanging the transverse resolution of the imaging system, or by alteringthe time separation of measurements T. For example, if retinal imagingwas changed to have a time separation of 10 ms with a 30 μm beamdiameter, the dynamic range of quantitative transverse flow calculationsis approximately 0.6 mm/s to 2.4 mm/s. If the statistics for the phasemeasurement in this case was sufficient, the phase data acquired with 10ms time separation can be used to calculate phase variance for a timeseparation of 20 ms as well (every second phase position measured). Thiswould increase the dynamic range of the quantitative flow measurement bya factor of 2, which in this case results in approximately 0.3 mm/s to2.4 mm/s.

A second method of determining transverse flow uses the fact that therefractive index variations of transverse flow regions create shadowingartifacts below the flow within the image. If the time separation forthe onset can be identified, the depth extent of the flow region can beidentified, and the average index of refraction change within the regioncan be identified. With knowledge of the refractive indices of thescatterers within the flow region, transverse flow rates can beidentified. This method is likely to be useful for cases where the flowregions contain multiple types of constituents, like plasma and bloodcells within blood vessel flow.

Another method of quantitatively determining transverse flow uses acombination of BM-scans and MB-scans over a region of the sample. TheBM-scan phase variance contrast is a highly efficient method ofidentifying three-dimensional regions of mobility within a sample. Withthe BM-scans, three-dimensional vasculature can be identified and thedirectionality of the flow relative to the imaging direction can beused. The MB-scan can be used over the identified vessel of interest andselectively analyzing that specific regional flow. The average axialflow determined from the average phase change can determine the flowwithin the region. With the location identified by the screening methodand the increased statistics available from the MB-scan, small axialflow components can be calculated. With the directionality of the vesselknown, the flow within the vessel can be geometrically calculated. Thetemporal evolution of the phase variance can provide additionalcorrelation to the flow calculation through an estimation of thetransverse component of the flow.

Transverse Motion Noise Removal

One of the issues associated with the acquisition methods is theadditional noise which accompanies the increased time separation. Whilethe scatterer motion is given more time to move between phasemeasurements, the sample is also given more time to move as well. Theaxial motion of the sample is removed through bulk motion removalmethods described earlier. The bulk transverse motion is not compensatedfor with the previous method. As derived in literature, relativetransverse motion between the sample and the imaging beam creates aphase error depending on the magnitude of fraction of the imaging beamwaist which has been moved [1]. The derivation of the phase noise isbased upon the assumption of uncorrelated scatterers within the sample,which is not the case for all reflections within layers samples like theretina.

FIG. 14 demonstrates OCT images taken of a slice through a mouse retina,with the averaged OCT intensity image compared against the phasevariance contrast image. This contrast image used numerical estimates toremove the SNR-limited phase noise as well as applying median filteringto further reduce artifacts. The regions of motion in the contrast imageare clearly observed, including the top retinal vessels with contrastshadowing occurring below the vessels and the bottom choroidal vesselswhich do not have any OCT signal below to produce additional shadowing.In this contrast image, there is a minimal amount of bulk transversemotion occurring in the sample. That is not the case for the phasevariance contrast image in FIG. 15, which is taken at the sametransverse locations within the retina but at a later time when largertransverse motion is occurring. Within this image, visualization of theflow regions is still possible but there is a significant amount ofadditional noise which hinders visualization.

The approach used to deal with the bulk transverse motion is to assumethat the entire contrast image contains the same level of additionalphase noise at all transverse locations for the case of the BM-scan.Since the BM-scan acquires data at all transverse locations in a shorttime frame, all of these points should be experiencing the same motionand the same resulting phase noise. Using all of the contrast datapoints which are not zero after SNR phase noise removal, the statisticsof the contrast image data can be used to try and remove the effect ofthe transverse motion. For the mean and standard deviation of thenon-zero contrast data of the image of μ and σ, respectively, thecontrast image C(x,z) can be adjusted through many methods including aremoval and normalization of the form:

${\left( {{C\left( {x,z} \right)} - \left( {\mu + {\alpha\sigma}} \right)} \right)\left( \frac{3.3}{3.3 - \left( {\mu + {\alpha\sigma}} \right)} \right)} = {C^{\prime}\left( {x,z} \right)}$In this case, 3.3 radians² is the amount chosen as the phase variancecontrast value as the expected maximum value associated with a randomphase measurement. The parameter a is chosen to be adjustable to improvethe visualization of regions which may be removed by over-estimation ofthe phase noise. FIG. 16 demonstrates the above mentioned additionalphase removal for the case of α=0.

FIG. 17 demonstrates the noise removal process when applied to theretinal contrast images acquired over time. Each two-dimensionalcontrast image is summed over the entire depth and is presented as aone-dimensional line. The multiple images acquired over time create thetwo-dimensional contrast summation image over time for this sample. Theleft image shows vertical lines, which demonstrate the appearance ofadditional motion contrast noise within the BM-scan contrast image atthat point in time. The right image shows the contrast summation imagesover time after the transverse motion removal method described above,for the case of α=0. The variations in the vascular positions within theimage over time are caused by the transverse motion occurring duringimaging.

The phase noise estimation used for the numerical removal for the phasevariance contrast images is based on the measured phase noise as afunction of the reflectivity signal S². The measured OCT intensitysignal I² is a combination of the reflectivity signal S² and the noisesignal N². The averaged OCT intensity signal is of the form:

Ĩ| ²

=S ²

N ²

The measured phase noise and the expected form are plotted on FIG. 21.For S>>N, the phase noise shows the expected form as described earlier.

Additional methods which can be used to remove the transverse motionnoise involve using additional statistics to remove any contrast imagecontaining transverse motion noise. Using an external motion trackers orsoftware analysis of the OCT intensity and contrast images, BM-scan datacontaining significant transverse motion can be identified and removed.Repeating the BM-scan acquisition over the same region when lesstransverse motion is occurring eliminates the requirement of phaseremoval analysis.

Intermittent Flow Identification

One of the challenges of motion contrast screening is identifyingregions that do not contain scatterers all of the time. OCT requiresreflections within the sample to create the phase measurements and somesituations do not allow for measurements to be taken at any time. Thezebrafish and the segmental vessels are a good example of thisreflectivity requirement. The segmental vessels are 7-12 microns indiameter for the 3 dpf (days post fertilization) embryo, which branchoff of a larger vessel called the dorsal aorta. Confocal imaging of theembryo has shown that the blood cells are only present within specificlocations of these vessels for a fraction of the time. If OCT imagingoccurs at a transverse location when there are no blood cells presentwithin the vessel, there is not enough reflectivity from within thevessel to produce a phase contrast signal.

FIG. 18 demonstrates multiple OCT images taken through the tail and yolksac of the zebrafish at the same transverse scan location but atdifferent time points. The arrows in all of the images designate theexpected locations of the flow regions: the dorsal aorta (DA), the axialvein (AV), the segmental vessels (Se) and the dorsal longitudinal vessel(DLV). The locations of these flow regions is not evident through theOCT intensity image due to a lack of intensity contrast of these regionsas well as a lack of sufficient absorption by these tiny vessels. Thephase variance contrast images demonstrate the appearance of contrastfor the dorsal aorta and the axial vein for all presented images, butthe segmental vessels and the dorsal longitudinal vessel do not appearto have contrast at all times, as expected.

This situation is similar to the microvasculature in the retina, whichalso contains very small vasculature and does not contain motioncontrast at all points in time. As with any random phenomenon, multiplevisualization opportunities and increased statistics can assist in thevisualization of the events. With the efficiency of the BM-scanacquisition, this method can be repeated over regions expecting anintermittent flow situation, such as microvasculature, in order tovisualize the motion contrast.

Intensity/Speckle Contrast

All of the contrast analysis methods described so far have been directedtowards the variance of phase changes over time for scatterers. With thesame acquisition methods, there is available data of the OCT intensityinformation over time as well. Many types of sample properties canresult in intensity fluctuations within the image: light couplingchanges, source power fluctuations, and relative polarization changes inthe interferometer all can cause intensity variations over time.Examples of intensity fluctuations caused by sample motion include, butare not limited to:

-   -   Interference of reflections from multiple scatterers located        within the resolution of the system and small independent        scatterers like might be found in blood.    -   Locations of flow based on variations in the reflecting        constituents within the flow region over time.    -   Variations in absorption of regions of blood flow over time.        The intensity fluctuation and/or speckle analysis over time can        act in parallel with the phase variance analysis work occurring        for the same data acquired. In cases of high bulk transverse        motion of the sample, the intensity analysis might be more        useful in identifying locations of flow. One of the analysis        techniques available with the temporal intensity information is        the variance of the OCT intensity. In order to properly image        this variance contrast, the result needs to be normalized based        on the intensity information (e.g., the mean, the median, the        maximum or the minimum of the intensity). Published intensity        fluctuation and/or speckle analysis techniques use normalized        spatial fluctuations in single images as contrast, limiting the        spatial resolution of the contrast images. For a single time        separation T of the intensity measurements, the variance of the        intensity changes will identify contrast in regions of motion        and absorption changes (listed above). The systematic intensity        fluctuations mentioned earlier (e.g., coupling changes) also        cause contrast to be observed in static regions of the image.        One of the ways to try and reduce this unwanted fluctuation is        to use numerical estimates of the expected fluctuations based on        the OCT intensity and the structure of the sample. One form of        contrast with an estimated form of intensity fluctuations is:        (σ_(ΔI) ₂ ²(z _(i) ,T)−f(z _(i) ,I ²(z _(i))))/<I ²>        Another analysis technique for the intensity fluctuation        temporal information is to analyze using a Fourier transform to        determine the temporal distribution of the fluctuations. The        shape, width and amplitude of the spectral information from the        Fourier transform can be used to identify multiple mobility        parameters including but not limited to the average diameter of        scatterer size and diffusional constant.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a basic SDOCT system schematic representative of thesystem used for most of data presented herein. The low coherence lightsource S(k) is split between the reference and sample arms of the fiberinterferometer. The reflected light is collected and measured in thespectrometer, from which the depth reflectivity profile is calculated.

FIG. 2 shows simulated phase change data for bulk motion of ˜π radians(˜one quarter of the wavelength of the imaging light).

FIG. 3 is a schematic description of expected phase variancemeasurements for mobile scatterers for a range of time separations ofphase change measurements.

FIG. 4A illustrates a schematic of a sample corresponding with thenon-averaged OCT intensity image shown in FIG. 4B. To obtain the images,a 2% Agarose well is filled with intensity-matched 0.1% Intralipidsolution. The intensity contrast within the image is limited only to theedges of the wells and the air-water interface.

FIG. 5A is a phase variance image for phase change time separation ofT=40 μs and FIG. 5B is a phase variance image for phase change timeseparation of T=80 μs.

FIG. 6A is a phase variance image for phase change time separation ofT=200 μs and FIG. 6B is a phase variance image for phase change timeseparation of T=400 μs.

FIG. 7A is a phase variance image for phase change time separation ofT=800 μs and FIG. 7B is a phase variance image for phase change timeseparation of T=1.6 ms.

FIG. 8A is a phase variance contrast image for maximum phase change timeseparation of T₂=40T₁ and FIG. 8B is a phase variance contrast image formaximum phase change time separation of T₂=20T₁.

FIG. 9A is a phase variance contrast image for a maximum phase timeseparation of T₂=10T₁ and FIG. 9B is a phase variance contrast image fora maximum phase time separation of T₂=5T₁.

FIG. 10 shows phase variance data for a single scatterer in water.Displayed data is for spheres of diameter 0.5 μm, 2 μm and 5 μm. The 2μm case demonstrates the effect of a phase error due to a low OCT signalon the phase variance data from expected form.

FIG. 11A illustrates a schematic of transverse scan patterns for aMB-scan. FIG. 11B illustrates a schematic of transverse scan patternsfor a BM-scan.

FIG. 12A illustrates a MB-scan acquired over Zebrafish tail in the formof an OCT intensity image. FIG. 12B illustrates a MB-scan acquired overZebrafish tail in the form of a phase variance contrast image. Imagesizes are 900 μm by 325 μm. T₂=1 ms and T₁=40 μs.

FIG. 13A illustrates a BM-scan acquired over a Zebrafish tail in theform of an OCT intensity image. FIG. 13B illustrates a BM-scan acquiredover a Zebrafish tail in the form of a phase variance contrast image.The image size for both FIGS. 13A and B is 815 μm by 325 μm. Phase errorremoved in FIG. 13B, the contrast image, was numerically estimated to beT₂=40 ms in this case. Note that the image scale of the phase variancecontrast image is 4 times larger than in the MB-scan contrast image ofFIG. 12B.

FIG. 14A illustrates the averaged intensity image calculated for a timepoint undergoing low transverse motion from BM-scan data for retinaundergoing transverse motion. FIG. 14B illustrates the phase contrastimage, after noise removal and median filtering, generated from BM-scandata for retina undergoing transverse motion. FIG. 14B, the phasecontrast image, is taken from a time point experiencing very little bulktransverse motion.

FIG. 15 illustrates a phase variance contrast image for the case ofuncorrected large bulk transverse motion.

FIGS. 16A and B are a comparison of phase contrast images. FIG. 16Aillustrates an uncorrected large transverse motion case and FIG. 16Billustrates a corrected large transverse motion case with α=0 chosen.

FIGS. 17A and B are contrast summation images over time summed over thefull retinal depth, acquired over 2.6 s. FIG. 17A is an image before theadditional motion compensation for the α=0 case and FIG. 17B is an imageafter the additional motion compensation for the α=0 case.

FIG. 18A illustrates a BM-scan averaged OCT intensity image and FIGS. 18B, C, and D show three-phase variance contrast images acquired atdifferent time points. Each image was acquired within a total time of 50ms. The arrows correspond to locations of the dorsal longitudinalvessel, two different segmental vessels (Se), the dorsal aorta (DA), andthe axial vein (AV).

FIGS. 19A and B illustrate OCT imaging for the visualization of a 3 dpfzebrafish. FIG. 19(A), a brightfield microscopy image, and FIG. 19(B),an image of a 3 dpf zebrafish, both show the expected anatomicalfeatures of the zebrafish. The lines drawn in FIGS. 19(A) and 19(B) arerepresentative OCT image scan positions. Further analysis into theaverage flow and phase variations can improve the quality of theseimages.

FIG. 20A depicts an OCT intensity image showing the internal structureof the zebrafish from FIG. 19. FIG. 20B shows the flow inside the heartafter improved statistics reduce the phase noise low OCT signal pixelsin the image. FIG. 20C depicts a phase contrast image using phasevariance data after removing the phase error component. Note the clearappearance of the heart in FIG. 20(B) and the in-flow tracts on the yolksack in FIG. 20(C) at the arrows, which matches with expected regionsfrom FIG. 19(B).

FIG. 21 is a schematic of the directionality of flow relative to theimaging light. The axial flow component observed by Doppler OCTtechniques is designated by Vz.

FIG. 22 is an SNR-limited phase noise plotted versus averaged OCTintensity signal.

FIGS. 23A, B, C, and D illustrate several contrast images produced usingthe MB-scan acquisition method of data over the zebrafish tail. FIG. 23Ais an OCT intensity image showing structural information. The arrows inFIG. 23A depict the expected regions of motion for the image,corresponding to the two main blood vessels, the dorsal aorta and axialvein, along the fish. FIG. 23B is a phase variance contrast image thatuses T₂=1 ms and T₁=40 μs with a variance scale of 0 to 2 radians² andobserves the locations which are not visible on the Doppler OCT imagesuntil sufficient statistics make it just visible enough to be observed.The arrows in FIG. 23B depict the expected regions of motion for theimage. FIG. 23C is a Doppler flow image that uses a scale of ±0.12radians=±200 μm/s for the cases of 5 phase change averages. FIG. 23D isa Doppler flow image that uses a scale of ±0.12 radians=±200 μm/s forthe cases of 100 phase change averages. Even with the improvedvisualization created with the increased statistics, prior knowledge ofmotion locations assists in the visualization.

FIGS. 24A and B demonstrate the same region of the zebrafish as in FIGS.23A, B, C, and D, however, they use the BM-scan acquisition method witha time separation of T=10 ms. The total time of the data acquired forthe 200 transverse pixel image is 50 ms for the chosen parameters of theacquisition. Due to the reduced quantitative dynamic range of theDoppler OCT methods with this data, the images produced using thatmethod are not presented. FIG. 24A is the averaged OCT intensity imagecompared against FIG. 24B the phase variance contrast image, using 5total B-scans, a rank 1 median filter in each direction, and a variancescale of 0 to 3 radians². The arrows in each of the images correspond toidentified locations of dorsal aorta and axial vein. FIG. 24B, the phasevariance contrast image, clearly observes the same motion regions as theMB-scan, but with additional shadowing below the vessels due torefractive index changes of the vasculature.

FIG. 25 displays a phase contrast summation image over time for atransverse slice located over the zebrafish heart for a total time of2.6 s. Each time point is acquired in 50 ms. This method was also usedin FIG. 17. The variations in the vasculature and in the heart can beclearly seen. The quantitative measure of the heart summation contrastchange over time is shown in FIG. 26. The variations in contrast, whichcorrespond to the changes in the size of the flow region as well as thechanges in the flow observed within the region follow the expected heartrate of the zebrafish.

FIG. 26 illustrates contrast over time for the zebrafish heart at onetransverse location. Contrast was summed over 3 transverse pixels (7.2μm) and over the entire depth of the zebrafish.

FIGS. 27A, B, and C are en face images over zebrafish heart. FIG. 27A isan OCT intensity summation image is presented in logarithmic scale. FIG.27B is the phase variance summation image is presented in logarithmicscale to improve visualization for comparison with FIG. 27C, a similarregion of a confocal image of GFP-labeled 3 dpf zebrafish. FIG. 27C, theconfocal image of fluorescent dye injected in a similarly agedzebrafish, is shown as a correlation to the imaged vasculature withinthe motion contrast image, FIG. 27B.

FIG. 28A demonstrates the averaged OCT intensity image and FIG. 28Bshows the Doppler flow image from successive phase changes using 5averages acquired over the mouse retina using the MB-scan. FIG. 28B, theDoppler flow image, does not use any thresholds and has a scale whichcorresponds to ±2.5 mm/s. Fast flow within the major retinal vessels isobserved in the axial flow measurement, but choroidal flow is notobserved using this image analysis technique. FIG. 28A demonstratesphase variance contrast images for a variety of phase change timeseparations. The increase in time separation corresponds to an increasein vascular visualization (choroidal vessels) as well as an increase incontrast shadowing below vasculature. The increased time separation alsocorresponds to an increased sensitivity to transverse motion, causingsome vertical lines of contrast.

FIG. 29A is a MB-scan phase variance contrast for the case of 40 μs timeseparation with 10 phase changes to calculate variance. FIG. 29B is aMB-scan phase variance contrast for the case of 160 μs time separationwith 40 phase changes to calculate variance. FIG. 29C is an MB-scanphase variance contrast for the cases of 240 μs time separation with 40phase changes to calculate variance. FIG. 29D is a MB-scan phasevariance contrast for the case of 320 μs time separation with 40 phasechanges to calculate variance. The images in FIGS. 29A, B, C, and Ddemonstrate the flattening of the retina used to remove the curvature ofthe images associated with changes of optical path length with thecurvature of the globe of the mouse eye. Flattening of the retinalimages and retinal layer separation through boundary identification inimages are the two main ways to extract information from depth regionsin the three-dimensional intensity and contrast data to createtransverse or en face images from the data set.

FIG. 30A illustrates an averaged B-scan intensity image beforerealignment to flatten retina. FIG. 30B illustrates an averaged B-scanintensity image after realignment to flatten retina.

FIGS. 31A, B, and C show BM-scan images for mouse retina. FIG. 31A isthe averaged intensity image. FIG. 31B is the phase variance imagewithout any numerical phase error removal or image filtering. FIG. 31Cis the phase variance contrast image after the application of noiseremoval and median filtering. The variance images use the scale 0 to 3radians².

FIG. 32A displays a BM-scan en face intensity image summed over theentire retina. FIG. 32B displays a BM-scan en face phase contrast imagesummed over the entire retina.

FIG. 33A displays en face summation image of the intensity and FIG. 33Bdisplays the phase variance contrast, with summation chosen to be overonly the top half of the retina. FIG. 33B, the contrast image,corresponds to the surface retinal vessels. The arrows correspond tolocations of identified microvasculature.

FIG. 34A displays en face depth summation images of the intensity andFIG. 34B displays the phase variance contrast, summed over the bottomhalf (choroidal region) of the retina. FIG. 34B, the contrast image,corresponds to the choroidal vessels as well as the shadowing contrastof the major retinal vessels. The arrows correspond to locations ofidentified microvasculature.

FIG. 35A shows a BM-scan en face summation image of intensity and FIG.35B shows BM-scan en face summation image of phase variance contrast.FIG. 35A, the intensity image, was summed over the entire retina, whileFIG. 35B, the contrast image, was summed over the top half of the retinafor the 200 transverse pixel BM-scan data across retina. FIG. 35B, thecontrast image, is a single 200×51 pixel image created by summing thetop of the retina from a single acquisition over the retina. The arrowsidentify some of the smaller visible blood vessels.

FIGS. 36A, B, C, and D show four en face phase contrast summation imagescreated from the top of the retinal data acquired from the repeating 100transverse pixel BM-scan method. The images show 100×50 pixel imagesextrapolated to 100×100 of contrast summation over the top of theretina, acquired successively during a single BM-scan acquisition of thesystem. The arrows highlight some of the microvasculature which isvisible in that given image which does not appear in all of the otherimages due to the intermittent nature of the contrast. The transversescan region of the image is identical to the region of the image inFIGS. 34A and 34B.

FIGS. 37A and B display the mean contrast summation images from twodifferent acquisitions of the repeating BM-scan method, summed over thetop half of the retina. Images in FIGS. 37A and B were acquired withperpendicular primary transverse scan directions. Images wereextrapolated to the size of 100×100 pixel for comparison.

While the description above refers to particular techniques forperforming the inventive method, it should be readily apparent to peopleof ordinary skill in the art that a number of modifications may be madewithout departing from the spirit thereof. The presently disclosedembodiments are, therefore, to be considered in all respects asillustrative and not restrictive.

REFERENCES

-   [1] B. Hyle Park, Mark C. Pierce, Barry Cense, Seok-Hyun Yun, Mircea    Mujat, Guillermo J. Tearney, Brett E. Bouma, Johannes F. de Boer,    “Real-time fiber-based multi-functional spectral domain optical    coherence tomography at 1.3 μm,” Opt. Express Vol. 13, No. 11,    3931-3944 (2005)

1. A method of diagnosing disease in a patient in need thereofcomprising: (i) ascertaining motion contrast in an area of the patientin need thereof comprising: (a) acquiring data using the opticalcoherence tomography system by performing one or more scan of the sample(b) ascertaining phase variance of the data, and (c) ascertaining themotion contrast in the sample based upon the phase variance; (ii)determining blood flow in the area based upon the motion contrast, and(iii) diagnosing the patient based upon the blood flow.
 2. The method ofclaim 1, wherein the step of ascertaining phase variance of the datacomprises using the temporal evolution of the measured phase variance ofthe motion in the sample to identify and characterize mobile scattererswithin the images obtained from one or more scan of the sample.
 3. Themethod of claim 1, wherein the method further comprises ascertaining thetemporal fluctuations in intensity of the data acquired using theoptical coherence tomography system and ascertaining the motion contrastbased upon the temporal fluctuation.
 4. The method of claim 1, whereinthe method further comprises estimating refractive index variationswithin flow regions and ascertaining the motion contrast based upon theestimate.
 5. The method of claim 4, wherein estimating refractive indexvariations comprises time separation of the onset of refractive indexshadowing within one or more phase contrast image and the refractiveindicies of the constituents within the flow region.
 6. The method ofclaim 1, wherein the scan is a MB-scan, a BM-scan, or both.
 7. Themethod of claim 1, wherein the optical coherence tomography systemcomprises a Fourier domain optical coherence tomography system.
 8. Themethod of claim 7, wherein the Fourier domain optical coherencetomography system comprises spectral domain optical coherencetomography, swept source optical coherence tomography, or opticalfrequency domain imaging.
 9. A method for diagnosing an eye disease in apatient in need thereof by the method of claim
 1. 10. The method ofclaim 9, wherein the eye disease is a disease of the retina.
 11. Themethod of claim 10, wherein the disease of the retina is any one or moreof glaucoma, macular degeneration, diabetic retinopathy or defectiveaqueous humor production.
 12. A method for diagnosing vascular diseasein a patient in need thereof by the method of claim 1.